Deep learning-based fall detection

In the modern information era, fall accidents are one of the leading causes of injury, disability and death to elderly individuals. This research focuses on object detection and recognition using deep neural networks, which is applied to the theme of fall detection. We propose a deep learning algorithm with the capability to detect fall accidents based on the state-of-the-art object detector, YOLOv3. Our system is tested on a challenging video database with diverse fall accidents under different scenarios and achieves an overall accuracy rate of 63.33%. The proposed deep network shows great potential to be deployed in real-world scenarios for health monitoring

An Enhanced Intelligent Agent with Image Description Generation

In this paper, we present an Embodied Conversational Agent (ECA) enriched with automatic image understanding, using vision data derived from state-of-the-art machine learning techniques for the advancement of autonomous interaction with the elderly or infirm. The agent is developed to conduct health and emotion well-being monitoring for the elderly. It is not only able to conduct question-answering via speech-based interaction, but also able to provide analysis of the user’s surroundings, company, emotional states, hazards and fall actions via visual data using deep learning techniques. The agent is accessible from a web browser and can be communicated with via voice means, with a webcam required for the visual analysis functionality. The system has been evaluated with diverse real-life images to prove its efficiency